Journal ArticleDOI
Brain tumor detection using statistical and machine learning method.
TLDR
The presented approach outperformed as compared to existing approaches in segmentation and specificity, sensitivity, accuracy, area under the curve (AUC) and dice similarity coefficient (DSC) at the fused feature based level.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2019-08-01. It has received 125 citations till now.read more
Citations
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Journal ArticleDOI
Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images
TL;DR: An active deep learning-based feature selection approach is suggested to segment and recognize brain tumors and shows that the presented method outperforms for both segmentation and classification of brain tumors.
Journal ArticleDOI
Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.
TL;DR: The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques.
Journal ArticleDOI
A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection
TL;DR: This work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices and confirms that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair, T2, T1C and clinical images.
Journal ArticleDOI
Brain tumor detection and multi-classification using advanced deep learning techniques
TL;DR: In this article, the authors presented segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU).
Journal ArticleDOI
Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification.
TL;DR: In this paper, a deep learning approach was proposed to classify brain tumors using an MRI data analysis to assist practitioners, which comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (ie, 19 layered Visual Geometric Group) model Moreover, the synthetic data augmentation concept was introduced to increase available data size for classifier training.
References
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Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Journal ArticleDOI
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas,Christian Ledig,Virginia F. J. Newcombe,Joanna P. Simpson,Andrew D. Kane,David K. Menon,Daniel Rueckert,Ben Glocker +7 more
TL;DR: An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.
Journal ArticleDOI
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei,Axel Davy,David Warde-Farley,Antoine Biard,Aaron Courville,Yoshua Bengio,Chris Pal,Pierre-Marc Jodoin,Hugo Larochelle +8 more
TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.
Journal ArticleDOI
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
TL;DR: This paper proposes an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels, which allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network.
Journal ArticleDOI
A survey of MRI-based medical image analysis for brain tumor studies
TL;DR: The state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas is reviewed, giving special attention to recent developments in radiological tumor assessment guidelines.
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